Breast Cancer Detection Using Six Different Algorithms
Deshpande Arnav Sunil, Saloni Parekh, Anish Pattnaik, Ayushmaan Agarwal, Dr. C. G. Mohan · 2022
Cancer is among the most prevalent causes of mortality worldwide.Breast cancer is one of most often diagnosed cancers, with over 200 different varieties to choose from.Given the high prevalence of morbidity and death, early and accurate diagnosis are critical.For this purpose, symptoms must be carefully assessed and classified and this can be done by Machine Learning (ML) and Data Analytics techniques.The main goal of this research is to examine several ML and Deep Learning (DL) approaches for breast cancer diagnosis and accuracy prediction.The primary dataset being used for research purposes is the Wisconsin Breast Cancer Diagnosis dataset.The following are the findings of the algorithms used: 93.08 percent accuracy for Linear Regression, 93.61 percent accuracy for the K Nearest Neighbor, 96.50 percent accuracy for Support Vector Machine, and 95.10 percent accuracy for Multilayer Perceptron approaches.